{"ID":2840001,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.14398","arxiv_id":"2511.14398","title":"Stage Aware Diagnosis of Diabetic Retinopathy via Ordinal Regression","abstract":"Diabetic Retinopathy (DR) has emerged as a major cause of preventable blindness in recent times. With timely screening and intervention, the condition can be prevented from causing irreversible damage. The work introduces a state-of-the-art Ordinal Regression-based DR Detection framework that uses the APTOS-2019 fundus image dataset. A widely accepted combination of preprocessing methods: Green Channel (GC) Extraction, Noise Masking, and CLAHE, was used to isolate the most relevant features for DR classification. Model performance was evaluated using the Quadratic Weighted Kappa, with a focus on agreement between results and clinical grading. Our Ordinal Regression approach attained a QWK score of 0.8992, setting a new benchmark on the APTOS dataset.","short_abstract":"Diabetic Retinopathy (DR) has emerged as a major cause of preventable blindness in recent times. With timely screening and intervention, the condition can be prevented from causing irreversible damage. The work introduces a state-of-the-art Ordinal Regression-based DR Detection framework that uses the APTOS-2019 fundus...","url_abs":"https://arxiv.org/abs/2511.14398","url_pdf":"https://arxiv.org/pdf/2511.14398v1","authors":"[\"Saksham Kumar\",\"D Sridhar Aditya\",\"T Likhil Kumar\",\"Thulasi Bikku\",\"Srinivasarao Thota\",\"Chandan Kumar\"]","published":"2025-11-18T12:02:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}